1. Database Design and MySQL
Session 4
INFM 718N
Web-Enabled Databases

2. Agenda Database design MySQL Project teams: next steps
(if we have time) Programming

3. Server-side Programming Interchange Language Client-side Programming
Relational normalization
Structured programming
Software patterns
Object-oriented design
Functional decomposition
Client Hardware
(PC, Unix)
(MySQL)
(PHP)
(HTML, XML)
(JavaScript)
(IE, Firefox)
(PC)
Business
rules
Interaction
Design
Interface
Web Browser
Server-side Programming
Interchange Language
Client-side Programming
Database
Server Hardware

4. Databases Database DataBase Management System (DBMS)
Collection of data, organized to support access
Models some aspects of reality
DataBase Management System (DBMS)
Software to create and access databases
Relational Algebra
Mathematical theory that supports optimization

5. Database “Programming”
Structured Query Language (SQL)
Consistent, unambiguous interface to any DBMS
Simple command structure:
e.g., SELECT last-name FROM students WHERE dept=“CLIS”
Useful standard for inter-process communications
Visual programming (e.g., Microsoft Access)
Unambiguous, and easier to learn than SQL
Natural language (e.g., interactive voice response system)
Improves ease of use, but with potential for ambiguity and error
e.g., Show me the last names of students in CLIS

6. Getting Started What questions must you answer?
What data is needed to generate the answers?
Entities
Attributes of those entities
Relationships
Nature of those relationships
How will the user interact with the system?
Relating the question to the available data
Expressing the answer in a useful form

7. An E-R Example manage-role student team human implement-role client1 M
member-of 1
student
team 1 M
creates
human
implement-role 1 1
needs M
client
project d
php-project
ajax-project

8. E-R Diagrams Entities Relationships Types Attributes Identifier
Subtypes (disjoint / overlapping), aggregation
Attributes
Mandatory / optional
Identifier
Relationships
Cardinality
Existence
Degree

9. Making Tables from E-R Diagrams
Pick a primary key for each entity
Build the tables
One per entity
Plus one per M:M relationship
Choose terse but memorable table and field names
Check for parsimonious representation
Relational “normalization”
Redundant storage of computable values
Implement using a DBMS

10. Table-Oriented Lingo Field An “atomic” unit of data
number, string, true/false, …
Record A collection of related fields
Table A collection of related records
Each record is one row in the table
Each field is one column in the table
Primary Key The field that identifies a record
Values of a primary key must be unique
Database A collection of tables

11. Relational Lingo Tables represent “relations”
Course, course description
Name, address, department
Named fields represent “attributes”
Each row in the table is called a “tuple”
The order of the rows is not important
Queries specify desired conditions
The DBMS then finds data that satisfies them

12. Visualizing Tables
primary key

13. Key Lingo “Primary Key” uniquely identifies a record
e.g. student ID in the student table
“Compound” primary key
Synthesize a primary key with a combination of fields
e.g., Student ID + Course ID in the enrollment table
“Foreign Key” is primary key in the other table
Note: it need not be unique in this table

14. Goals of “Normalization”
Save space
Save each fact only once
More rapid updates
Every fact only needs to be updated once
More rapid search
Finding something once is good enough
Avoid inconsistency
Changing data once changes it everywhere

15. Normalization 1NF: Single-valued indivisible (atomic) attributes
Split “Doug Oard” to two attributes as (“Doug”, “Oard”)
Model M:M implement-role relationship with a table
2NF: Attributes depend on complete primary key
(id, impl-role, name)->(id, name)+(id, impl-role)
3NF: Attributes depend directly on primary key
(id, addr, city, state, zip)->(id, addr, zip)+(zip, city, state)
4NF: Divide independent M:M tables
(id, role, courses) -> (id, role) + (id, courses)
5NF: Don’t enumerate derivable combinations

16. Normalized Table Structure
Persons: id, fname, lname, userid, password
Contacts: id, ctype, cstring
Ctlabels: ctype, string
Students: id, team, mrole
Iroles: id, irole
Rlabels: role, string
Projects: team, client, pstring

17. Referential Integrity
“Foreign key” values must exist in another table
If not, those records cannot be joined
Checked when data added to this table
MySQL “Error 150”
Triggers when data deleted/changed in other table
Specify SET NULL, RESTRICT or CASCADE

18. Getting started with MySQL
“root” creates database, grants permissions
By you on WAMP (mysql –u root –p)
By Charles Goldman on OTAL
CREATE DATABASE team1;
GRANT SELECT, INSERT, UPDATE, DELETE, INDEX, ALTER, CREATE, DROP ON team1.* TO IDENTIFIED BY ‘bar’;
FLUSH PRIVILEGES;
Start mysql
Start->Run->cmd for WAMP, ssh for OTAL
mysql –u foo –p bar [you can cd to your playspace first, but you don’t need to]
Connect to your database
USE team1;

19. Some Useful MySQL Commands
Looking around
SHOW DATABASES;
SHOW TABLES;
DESCRIBE tablename;
SELECT * FROM tablename;
Optimization
SHOW TABLE STATUS \G;
OPTIMIZE TABLE tablename;
EXPLAIN <SQLquery>;
ALTER TABLE tablename ADD INDEX fieldname;

20. Creating Tables To delete: DROP TABLE contacts;
CREATE TABLE contacts (
ckey MEDIUMINT UNSIGNED NOT NULL AUTO_INCREMENT,
id MEDIUMINT UNSIGNED NOT NULL,
ctype SMALLINT UNSIGNED NOT NULL,
cstring VARCHAR(40) NOT NULL,
FOREIGN KEY (id) REFERENCES persons(id) ON DELETE CASCADE,
FOREIGN KEY (ctype) REFERENCES ctlabels(ctype) ON DELETE RESTRICT,
PRIMARY KEY (ckey)
) ENGINE=INNODB;
To delete: DROP TABLE contacts;

21. Populating Tables To empty a table: DELETE FROM ctlabels;
INSERT INTO ctlabels
(string) VALUES
('primary '),
('alternate '),
('home phone'),
('cell phone'),
('work phone'),
('AOL IM'),
('Yahoo Chat'),
('MSN Messenger'),
(‘other’);
To empty a table: DELETE FROM ctlabels;

22. The SQL SELECT Command SELECT (“projection”) chooses columns
Based on their label
WHERE (“restriction”) chooses rows
Based on their contents
e.g. department ID = “HIST”
These can be specified together
SELECT Student ID, Dept WHERE Dept = “History”

23. WHERE Clause Each SELECT contains a single WHERE Numeric comparison
<, >, =, <>, …
e.g., grade<80
Boolean operations
e.g., Name = “John” AND Dept <> “HIST”

24. A Denormalized “Flat File”

25. A Normalized Relational Database
Student Table
Department Table
Course Table
Enrollment Table

26. Example of Join
Student Table
Department Table
“Joined” Table

27. Project
New Table
SELECT Student ID, Department

28. Restrict
New Table
WHERE Department ID = “HIST”

29. What are Requirements? Attributes Behavior Appearance
Concepts (represented by data)
Behavior
What it does
How you control it
How you observe the results

30. Who Sets the Requirements?
People who need the task done (customers)
People that will operate the system (users)
People who use the system’s outputs
People who provide the system’s inputs
Whoever pays for it (requirements commissioner)

31. The Requirements Interview
Focus the discussion on the task
Look for entities that are mentioned
Discuss the system’s most important effects
Displays, reports, data storage
Learn where the system’s inputs come from
People, stored data, devices, …
Note any data that is mentioned
Try to understand the structure of the data
Shoot for the big picture, not every detail

32. First Things First
Functionality
Content
Usability
Security/Stability

33. Language Learning Learn some words
Put those words together in simple ways
Examine to broaden your understanding
Create to deepen your mastery
Repeat until fluent

34. Thinking About PHP Local vs. Web-server-based display
HTML as an indirect display mechanism
“View Source” for debugging
Procedural perspective (vs. object-oriented)

35. Arrays in PHP A set of key-element pairs
$days = array(“Jan”->31, “Feb”=>28, …);
$months = explode(“/”, “Jan/Feb/Mar/…/Dec”);
$_POST
Each element is accessed by the key
{$days[“Jan”]}
$months[0];
Arrays and loops work naturally together

36. Thinking about Arrays Naturally encodes an order among elements
$days = rksort($days);
Natural data structure to use with a loop
Do the same thing to different data
PHP unifies arrays and hashtables
Elements may be different types

37. Functions in PHP Declaration Invoking a method
function multiply($a, $b=3){return $a*$b;}
Invoking a method
$b = multiply($b, 7);
All variables in a function have only local scope
Unless declared as global in the function

38. Why Modularity? Limit complexity Minimize duplication Extent
Interaction
Abstraction
Minimize duplication

39. Using PHP with (X)HTML Forms
<form action=“formResponseDemo.php”, method=“post”>
<input type=“text”, name=“ ”, value=“<?php echo $ ?>”, size=30 />
<input type=“radio”, name=“sure”, value=“yes” /> Yes
<input type=“radio”, name=“sure”, value=“no” /> No
<input type=“submit”, name=“submit”, value=“Submit” />
<input type=“hidden”, name=“submitted”, value=“TRUE” />
</form>
if (isset($_POST[“submitted”])) {
echo “Your address is $ .”;
} else {
echo “Error: page reached without proper form submission!”; }

40. Sources of Complexity Syntax
Learn to read past the syntax to see the ideas
Copy working examples to get the same effect
Interaction of data and control structures
Structured programming
Modularity

41. Some Things to Pay Attention To
Syntax
How layout helps reading
How variables are named
How strings are used
How input is obtained
How output is created
Structured Programming
How things are nested
How arrays are used
Modular Programming
Functional decomposition
How functions are invoked
How arguments work
How scope is managed
How errors are handled
How results are passed

42. Programming Skills Hierarchy
Reusing code [run the book’s programs]
Understanding patterns [read the book]
Applying patterns [modify programs]
Coding without patterns [programming]
Recognizing new patterns

43. Best Practices Design before you build Focus your learning
Program defensively
Limit complexity
Debug syntax from the top down

44. Rapid Prototyping + Waterfall
Update
Requirements
Write
Specification
Initial
Requirements
Choose
Functionality
Create
Software
Build
Prototype
Write
Test Plan

45. Focus Your Learning Find examples that work
Tutorials, articles, examples
Cut them down to focus on what you need
Easiest to learn with throwaway programs
Once it works, include it in your program
If it fails, you have a working example to look at

46. Defensive Programming
Goal of software is to create desired output
Programs transform input into output
Some inputs may yield undesired output
Methods should enforce input assumptions
Guards against the user and the programmer!
Everything should be done inside methods

47. Limiting Complexity Single errors are usually easy to fix
So avoid introducing multiple errors
Start with something that works
Start with an existing program if possible
If starting from scratch, start small
Add one new feature
Preferably isolated in its own method

48. Types of Errors Syntax errors Run time exceptions Logic errors
Detected at compile time
Run time exceptions
Cause system-detected failures at run time
Logic errors
Cause unanticipated behavior (detected by you!)
Design errors
Fail to meet the need (detected by stakeholders)

49. Debugging Syntax Errors
Focus on the first error message
Fix one thing at a time
The line number is where it was detected
It may have been caused much earlier
Understand the cause of “warnings”
They may give a clue about later errors
If all else fails, comment out large code regions
If it compiles, the error is in the commented part

50. Run Time Exceptions Occur when you try to do the impossible
Use a null variable, divide by zero, …
The cause is almost never where the error is
Why is the variable null?
Exceptions often indicate a logic error
Find why it happened, not just a quick fix!

51. Debugging Run-Time Exceptions
Run the program to get a stack trace
Where was this function called from?
Print variable values before the failure
Reason backwards to find the cause
Why do they have these values?
If necessary, print some values further back

52. Logic Errors Evidenced by inappropriate behavior
Can’t be automatically detected
“Inappropriate” is subjective
Sometimes very hard to detect
Sometimes dependent on user behavior
Sometimes (apparently) random
Cause can be hard to pin down

53. Debugging Logic Errors
First, look where the bad data was created
If that fails, print variables at key locations
if (DEBUG) echo “\$foobar = $foobar”;
Examine output for unexpected patterns
Once found, proceed as for run time errors
define (“DEBUG”, FALSE); to clean the output

54. Three Big Ideas Functional decomposition High-level languages Patterns
Outside-in design
High-level languages
Structured programming, object-oriented design
Patterns
Design patterns, standard algorithms, code reuse

55. One-Minute Paper What was the muddiest point in today’s class?
Be brief!
No names!